../data/customer-segmentation.ipynb).../data/data.csv).The work is done with the help of the nbdev tool, which allows you to develop a python library in Jupyter Notebooks, putting all your code, tests and documentation in one place.
pip install -U git+https://github.com/artemlops/featurologists.git@master
import pandas as pd
from pathlib import Path
import datetime
from featurologists.data.load_split import load_data_csv, split_by_invoice_date
ONLINEOFFLINE_DATE_SPLIT = datetime.date(2011,10,1)
# Loading original dataset
df = load_data_csv('../data/data.csv')
print(f'Loaded dataset, shape: {df.shape}')
# Splitting dataset to offline and online parts
df_offline, df_online = split_by_invoice_date(df, ONLINEOFFLINE_DATE_SPLIT)
print(f'Offline dataset shape: {df_offline.shape}')
print(f'Offline invoices: from {df_offline["InvoiceDate"].min()} to {df_offline["InvoiceDate"].max()}')
print(f'Online dataset shape: {df_online.shape}')
print(f'Online invoices: from {df_online["InvoiceDate"].min()} to {df_online["InvoiceDate"].max()}')
OUTPUT = Path(f'../data/output/01_data_split_offline_online')
OUTPUT.mkdir(exist_ok=True, parents=True)
df_offline.to_csv(f'{OUTPUT}/no_live_data.csv', index=False)
df_online.to_csv(f'{OUTPUT}/raw_live_data.csv', index=False)
Path(f'{OUTPUT}/onlineoffline_date_split.txt').write_text(str(ONLINEOFFLINE_DATE_SPLIT))
print(f'Output data saved to {OUTPUT}: {[p.name for p in Path(OUTPUT).iterdir()]}')
from featurologists.data.load_split import load_data_csv
from featurologists.data.clean_rows import clean_data_rows
# Loading raw offline dataset'
df = load_data_csv('../data/output/01_data_split_offline_online/no_live_data.csv')
print(f'Loaded raw offline dataset, shape: {df.shape}')
# Cleaning the dataset
df_cleaned = clean_data_rows(df)
print(f'Cleaned offline dataset shape: {df.shape}')
OUTPUT = Path(f'../data/output/02_data_clean_rows')
OUTPUT.mkdir(exist_ok=True, parents=True)
df_cleaned.to_csv(f'{OUTPUT}/no_live_data__cleaned.csv', index=False)
print(f'Output data saved to {OUTPUT}: {[p.name for p in Path(OUTPUT).iterdir()]}')
import datetime
from featurologists.data.load_split import load_data_csv
from featurologists.data.analyse_purchases import build_product_list
N_PURCHASE_CLUSTERS = 5
TRAINTEST_DATE_SPLIT = datetime.date(2011,8,1)
# Loading cleaned dataset
df_cleaned = load_data_csv('../data/output/02_data_clean_rows/no_live_data__cleaned.csv')
print(f'Loaded cleaned offline dataset, shape: {df_cleaned.shape}')
list_products = build_product_list(df_cleaned)
print(f'Built list of products:')
print(pd.DataFrame(list_products).head())
print('...')
from featurologists.data.analyse_purchases import build_keywords_matrix
# Building keywords count matrix
THRESHOLD = [0, 1, 2, 3, 5, 10]
matrix = build_keywords_matrix(df_cleaned, list_products, THRESHOLD)
print(f'Built keywords count matrix (shape: {matrix.shape}):')
print(matrix.head())
from featurologists.data.analyse_purchases import compute_purchase_clusters
# Computing purchases clusters via Kmeans
clusters = compute_purchase_clusters(matrix, N_PURCHASE_CLUSTERS)
print(f'Built purchase clusters:')
print(pd.Series(clusters).value_counts())
from sklearn.metrics import silhouette_samples, silhouette_score
from featurologists.data.analyse_purchases import plot_silhouette
silhouette_avg = silhouette_score(matrix, clusters)
sample_silhouette_values = silhouette_samples(matrix, clusters)
# Plotting silhouette values
plot_silhouette(N_PURCHASE_CLUSTERS, [-0.07, 0.33], len(matrix), sample_silhouette_values, clusters)
from featurologists.data.analyse_purchases import add_purchase_clusters_info
# Constructing the result DataFrame
df_with_clusters = add_purchase_clusters_info(df_cleaned, clusters, N_PURCHASE_CLUSTERS)
print(f'Added purchase clusters info to the offline cleaned dataset:')
print(f'Shape: {df_with_clusters.shape}')
print(f'Columns: {list(df_with_clusters.columns)}')
from featurologists.data.load_split import split_by_invoice_date
# Splitting the new dataset (offline + cluster info) to train+test
df_offline_train, df_offline_test = split_by_invoice_date(df_with_clusters, TRAINTEST_DATE_SPLIT)
print(f'Splitted: train of shape {df_offline_train.shape} + test of shape {df_offline_test.shape}')
OUTPUT = Path(f'../data/output/03_data_compute_description_keywords')
OUTPUT.mkdir(exist_ok=True, parents=True)
matrix.to_csv(f'{OUTPUT}/no_live_data__cleaned__keywords.csv', index=False)
df_offline_train.to_csv(f'{OUTPUT}/no_live_data__cleaned__purchase_clusters__train.csv', index=False)
df_offline_test.to_csv(f'{OUTPUT}/no_live_data__cleaned__purchase_clusters__test.csv', index=False)
pd.DataFrame(THRESHOLD, columns=['threshold']).to_csv(f'{OUTPUT}/threshold.csv', index=False)
Path(f'{OUTPUT}/n_purchase_clusters.txt').write_text(str(N_PURCHASE_CLUSTERS))
Path(f'{OUTPUT}/traintest_date_split.txt').write_text(str(TRAINTEST_DATE_SPLIT))
print(f'Output data saved to {OUTPUT}: {[p.name for p in Path(OUTPUT).iterdir()]}')
from featurologists.data.load_split import load_data_csv
N_CUSTOMER_CLUSTERS = 11
SELECTED_CUSTOMERS_CATEG_THRESHOLD = 40
# Loading cleaned offline train dataset
DATA = '../data/output/03_data_compute_description_keywords'
N_PURCHASE_CLUSTERS = int(Path(f'{DATA}/n_purchase_clusters.txt').read_text().strip())
basket_price = load_data_csv(f'{DATA}/no_live_data__cleaned__purchase_clusters__train.csv')
print(f'Loaded purchase clusters data of shape: {basket_price.shape}')
print(basket_price.head())
print('...')
from featurologists.data.analyse_customers import build_transactions_per_user
# Building transactions per user
transactions_per_user = build_transactions_per_user(basket_price, n_purchase_clusters=N_PURCHASE_CLUSTERS)
print(f'Built transactions per user, shape: {transactions_per_user.shape}')
print(transactions_per_user.head())
print('...')
from featurologists.data.analyse_customers import (
plot_customers_pca,
convert_customers_df_to_np,
analyse_customers_pca,
)
# Analysing customers distribution via PCA
matrix = convert_customers_df_to_np(transactions_per_user, N_PURCHASE_CLUSTERS)
scaled_matrix, pca = analyse_customers_pca(matrix)
plot_customers_pca(matrix, pca)
from featurologists.data.analyse_customers import compute_customer_clusters
# Computing customers clusters via Kmeans
clusters_clients = compute_customer_clusters(scaled_matrix, N_CUSTOMER_CLUSTERS)
print('Computed customers clusters via Kmeans:')
display(pd.Series(clusters_clients).value_counts())
from sklearn.metrics import silhouette_samples, silhouette_score
from featurologists.data.analyse_purchases import plot_silhouette
silhouette_avg = silhouette_score(scaled_matrix, clusters_clients)
sample_silhouette_values = silhouette_samples(scaled_matrix, clusters_clients)
# Plotting silhouette values
plot_silhouette(N_CUSTOMER_CLUSTERS, [-0.15, 0.55], len(scaled_matrix), sample_silhouette_values, clusters_clients)
from featurologists.data.analyse_customers import plot_customer_categories
# Plotting customers categories
plot_customer_categories(scaled_matrix, clusters_clients, N_CUSTOMER_CLUSTERS)
from featurologists.data.analyse_customers import add_customer_clusters_info
# Constructing the result dataset
merged_df = add_customer_clusters_info(transactions_per_user, clusters_clients)
print(f'Constructed the result dataset:')
print(f'Shape: {merged_df.shape}')
print(f'Columns: {list(merged_df.columns)}')
from featurologists.data.analyse_customers import compute_aggregated_customer_clusters_info
# Constructing the aggregated cluster info dataset
selected_customers_df = compute_aggregated_customer_clusters_info(merged_df, N_PURCHASE_CLUSTERS, N_CUSTOMER_CLUSTERS,
categ_threshold=SELECTED_CUSTOMERS_CATEG_THRESHOLD)
print('Constructed the aggregated cluster info:')
print(f'Shape: {selected_customers_df.shape}')
print(f'Columns: {list(selected_customers_df.columns)}')
OUTPUT = Path(f'../data/output/04_data_analyse_customers')
OUTPUT.mkdir(exist_ok=True, parents=True)
selected_customers_df.to_csv(f'{OUTPUT}/no_live_data__cleaned__purchase_clusters__train__selected_customers_aggregated.csv', index=False)
merged_df.to_csv(f'{OUTPUT}/no_live_data__cleaned__purchase_clusters__train__customer_clusters.csv', index=False)
Path(f'{OUTPUT}/n_customer_clusters.txt').write_text(str(N_CUSTOMER_CLUSTERS))
print(f'Output data saved to {OUTPUT}: {[p.name for p in Path(OUTPUT).iterdir()]}')
from sklearn.model_selection import train_test_split
INPUT = '../data/output/04_data_analyse_customers'
selected_customers = pd.read_csv(f'{INPUT}/no_live_data__cleaned__purchase_clusters__train__customer_clusters.csv')
X = selected_customers[['mean', 'categ_0', 'categ_1', 'categ_2', 'categ_3', 'categ_4' ]]
Y = selected_customers['cluster']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size = 0.8)
X_train.shape, X_test.shape, Y_train.shape, Y_test.shape